Transcript PATMOS-x Status and Comparison to Other Climatologies Michael Pavolonis (NOAA)

PATMOS-x Status and Comparison to
Other Climatologies
Andrew Heidinger (NOAA), Amato Evan (CIMSS)
Michael Pavolonis (NOAA)
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2nd Cloud Climatology Assessment Workshop, Madison WI, July 2006
Outline
• PATMOS-x Introduction / Status
• Spatial Anomaly Correlations
• Adjusted Time Series Comparisons
• Summary of our findings
• Future Work
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2nd Cloud Climatology Assessment Workshop, Madison WI, July 2006
PATMOS-x Introduction / Status
• PATMOS-x is an extension of the AVHRR Pathfinder Atmospheres (PATMOS) Project that
once developed in the 1990’s as part of the NASA/NOAA Pathfinder Initiative and supported by
NOAA OGP. PATMOS processed by SAA (now CLASS)
• Unlike PATMOS, PATMOS-x processes the data from morning orbiters and from the newer
NOAA-klm sensors.
• PATMOS-x grew out of the new AVHRR operational cloud processing system (CLAVR-x) so it
has a full-suite of cloud products (not just total cloud amount)
• PATMOS-x funded within NESDIS/ORA and also includes a significant data improvement effort
(calibration / navigation)
•Products include a full suite of cloud properties and some non-cloud properties (aerosol, SST,
NDVI and OLR).
• Data is averaged on a 55 km equal-area grid – though this is flexible. PATMOS was 110 km.
• Files are produced as HDF4. We make single satellite daily asc/des files and single or multiple
satellite monthly averaged files.
• Data available here http://cimss.ssec.wisc.edu/clavr/patmosx.html
•New reprocessing of entire data-set at NESDIS will begin shortly.
2nd Cloud Climatology Assessment Workshop, Madison WI, July 2006
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AVHRR AS A CLIMATE OBSERVATION PLATFORM
• AVHRR provides a 25 year record of 4 km global
High Cloud Diurnal Cycle: July 2004 NOAA-15,16,17
observations at 0.63, 0.86, 3.75, 10.8 and 12 mm.
• Since 1992, there have two 5-channel AVHRR’s
in orbit. Since 2000 there have been 3 in orbit.
• While orbital drift is an issue, processing both
morning and afternoon data aids in diurnal
sampling and perhaps improves daily average.
•Seek algorithmic solutions that are consistent from
satellite to satellite in a series (inter-annual) and for
all viewing geometries (seasonal and diurnal)
High Cloud Seasonal Cycle: NOAA-16 Des. 2004
High Cloud Inter-annual Cycle: July 1982-2004
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Current PATMOS-x Development
• We continuing to finalize our split-window approach to estimate
cloud emissivity and cloud height. This method gives us a day/night
independent approach. We have characterized its strengths/weakness
through comparison with MODIS.
• Exploring ways to make cloud typing more day/night independent.
• Added 2-d histograms including those that match ISCCP definitions.
• Exploring using a 3.75, 11 and 12 mm approach day and night to get
a diurnally resolved cloud microphysical information beyond what we
get from the split-window 11-12 mm approach.
• Continue to make infrared-only cloud amounts.
• Exploring better use of our multi-layer cloud information.
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Algorithm Work – Trying to Achieve Day/Night Consistency
We are trying to optimize the use of the AVHRR spectral information to make products
that are consistent for all conditions (day/night/term). We are willing to sacrifice
instantaneous performance for diurnal consistency.
For example, we are exploring use of 3.75, 11 and 12 mm observations to estimate
optical depth, microphysics and cloud height using the same algorithm for all
illumination conditions. Using same ice models as MODIS (Baum/Yang).
Example NOAA-15 Scene
In terminator conditions
Tropical Pacific
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• Most regions show a high level of
correlation (values near unity) and appear
red in the figures.
• The example on right shows mean
values and correlation of January Total
Cloud Amounts from PATMOS-x and
ISCCP. Only region with a lack of
correlation is Northern Eurasia – points to
a difference in cloud detection over snow
covered land.
PATMOS-x Mean
• The grid-cell anomaly time-series are
formed by subtracting a linear fit to each
grid-cell’s time series from the actual timeseries.
Anamoly Correlation
• Performed separately for each month
and for each grid-cell (2.5 degree)
ISCCP Mean
Anomaly Correlation Analysis
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Correlation of April Total Cloud Anomalies with PATMOS-x
ISCCP
UW HIRS
NCEP
ERA-40
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Correlation of October High Cloud Anomalies with PATMOS-x
ISCCP
UW HIRS
ERA-40
No NCEP High Cloud
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PATMOS-x Cloud Amount Time Series
Previous results showed the spatial patterns of relative agreement. Here we
analyze time series to explore more detailed temporal variation.
four time series are shown.
• Raw – no attempt to account for different observation times.
• Resampled – All data was resampled to a common observation time(s).
• Standardized Resampled – resampled time series are standardized by subtracting mean and
dividing by standard deviation.
• Standardized Resampled for Aqua/MODIS (2002-2004) – same except includes MODIS.
• We did this for PATMOS-x, ISCCP and MODIS and hope to extend to UW/HIRS if we can get
data separated by asc / des node.
• Amato will discuss this work further and many examples are on his web-site.
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Total Cloud Amount Over Water
Raw
ISCCP-vis
ISCCP-ir
MODIS
PATMOS-x
Resampled and Standardized
Resampled
2002-2004
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Total Cloud Amount Over Land
Raw
Resampled
2002-2004
Resampled and Standardized
ISCCP-vis
ISCCP-ir
MODIS
PATMOS-x
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High Cloud Over Water
Raw
Resampled
2002-2004
Resampled and Standardized
ISCCP-VIS
ISCCP-vis
ISCCP-IR
ISCCP-ir
MODIS
MODIS
PATMOS-x
PATMOS-x
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Comparison with non-satellite time series
We have included some analysis of climate model cloud fields (ERA40
and NCEP). In general ERA40 shows a higher degree of spatial
correlation but does show some long-term variability not observed in the
satellites
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Summary of Conclusions from our Comparisons
• Mean cloud amounts can vary significantly. Until we adopt a standard defintion
of cloud – this will always be the case.
• Anomalies of monthly means are very well correlated for most regions.
• Resampling the times series can make a big difference in the level of
agreement. This has shown that some of the features seen in PATMOS-x that
are due to orbit drift also appear in the resampled ISCCP. This indicates that
PATMOS-x is responding the same as ISCCP to the diurnal cycle.
• When standardized, many of the ISCCP, PATMOS-x and MODIS time-series
become very similar.
• Long term variation in total cloud amount over water still shows disagreement
after resampling and standarization (esp. pre 1998).
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Addition of PATMOS-x to Claudia’s Global Amount Tables
PATMOS-x (2004) ISCCP (84-04) TOVS-B (87-95)UW-HIRS (85-01)
Cloud type amounts (%)
all
glo bal
62
ocean
75
67
land
66
73
70
74
2.8
1.9
77
46
58
69
Thick Cirrus
2.8
2.4
2.8
3.5
Cirrus
19.2 27.3
18.2 26.9
21.5 27.8
70
High-level
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22.1 29.7 33.4 23
21.1 28.8 34.3 22
24.3 31.3 33.9
Mid-level
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18.0 12.1 11.5 13
17.9 10.3 10.5 11
18.1 16.6 11.8
Low-level
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25.5 30.9 29.7 35
29.1 35.1 32.0 13
16.5 20.5 24.2
PATMOS-x is comprised of NOAA-15 + NOAA-16 (4 times/day)
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Addition of PATMOS-x to Claudia’s Regional Amount Tables
PATMOS-x (2004) ISCCP (84-04) TOVS-B (87-95)UW-HIRS (85-01)
Cloud type amounts (%)
all
NH midl
61
tro pics
75
63
63
71
3.5
2.5
SH midl
68
73
75
74
74
79
Thick Cirrus
3.4
3.0
3.1
2.4
Cirrus
20.0 24.7
25.5 44.8
16.2 21.8
83
High-level
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23.4 27.7 32.8 35
29.0 47.3 44.6 22
19.3 24.2 33.5
Mid-level
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21.1 16.2 12.8 6.6
12.7 4.1
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26.7 14.8 15.1
Low-level
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25.7 27.1 29.7 23
19.6 20.6 25.6 39
36.2 38.7 34.6
5.1
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Future Work
Our work shows that PATMOS-x is well correlated with ISCCP and
MODIS, this opens up the possibility of…
 Using PATMOS-x time-series to correct day/night discontinuities
in ISCCP day/night high cloud
 Using ISCCP diurnal cycles to adjust PATMOS-x time-series for
orbit drift.
 Using MODIS to calibrate PATMOS-x results and create pseudo
MODIS time-series with the AVHRR.
 Using PATMOS-x to remove artifacts seen in ISCCP?
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Backup Slides
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Other PATMOS-x Time Series
While we are focusing on cloud amounts, other PATMOS-x cloud
product time series are available for analysis and appear stable.
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